Fuzzy Controllers With Neural Network Predictor for Second-Order Linear Systems With Time Delay

Abstract

Artificial intelligence methods are widely applied in advanced control practices such as fuzzy calculation-based intelligent control method, neural network-based predictive control method, etc. Control performances are expected to be improved such as: faster control speed, smaller steady-state error, and less repeated manual tuning workloads in harmful environments for engineers. Main works are as follows: Firstly, change ratio-based fuzzy adjusted PID (FA-PID) method is improved. The adjusted parameters of FA-PID are the multiplication result of the change ratio at the current control cycle and the control parameters at the time of previous adjustment cycle. Secondly, prediction of back propagation neural networks-based fuzzy-PID (BPNN-F-PID) and prediction of back propagation neural networks-based FA-PID (BPNN-FA-PID) are improved, in which the adjusted control parameters are calculated according to the predicted output of control system. Thirdly, comparative simulations of all the above methods are implemented, a series of better effects are found. Effects are as follows: The improved controllers have better performance such as faster control speed, better ability of anti-interference, restrained overshoot and smaller steady-state error.

A Fuzzy Neural Network Controller Using Compromise Features for Timeliness Problem

“…The disadvantage of neural network fuzzy control is that it can not handle and describe fuzzy information, besides, there are black box characteristics during learning and problem solving so that its work is not interpretable, so obviously it can not have a good usage of existing experience and experts' know-how. Moreover, through Figure 1 and Figure 2 about system block diagram of an neural network fuzzy PID controller studied in [33], [34], we can find that the neural network fuzzy controller requires higher accuracy and quantity samples which would take longer development period and higher cost for training [35], [36]. To address disadvantages of the above described fuzzy controllers, this paper proposes new design methods on basis of the principle about two outputs compromise [37]- [39], when the parameters of the controller need to be adjusted, only the compromise factor needs to be adjusted.…”

Research on Pneumatic Control of a Pressurized Self-Elevating Mat for an Offshore Wind Power Installation Platform

“…After the introduction of fuzzy rules and a neural network, the system can adjust parameters adaptively and various performances are improved [ 25 , 26 ]. By combining fuzzy control and artificial intelligence, the control performance of the controller can be improved [ 27 ]. Using the genetic algorithm to optimize the PID controller based on a fuzzy neural network and the genetic algorithm can improve the self-adaptive ability of the system [ 28 ].…”